Encoding data generation method and device

ABSTRACT

In a coded data generation method of resampling a road shape for setting a plurality of nodes, arranging position information of each node represented by a deflection angle from the immediately preceding node to generate a data string of the deflection angles, converting the deflection angles into predicted difference values each indicating the difference from the predicted value, and variable-length coding the data string of the predicted difference values, the data string of the predicted difference values when the deflection angles are converted into the predicted difference values is evaluated and a prediction expression to calculate the predicted value is selected adaptively from among a plurality of prediction expressions φ=1, φ=2, φ=3, and φ=4 based on the evaluation result. The prediction expression to calculate the predicted value is selected adaptively in response to road shape A, B, C, D, so that the data compression effectiveness is enhanced.

TECHNICAL FIELD

This invention relates to a generation method of coded data representinga road position, etc., on a digital map and an apparatus for generatingand decoding the coded data and is intended for reducing the data amountof the coded data.

BACKGROUND ART

Hitherto, VICS (Vehicle Information Communication System) has conductedthe service for providing vehicle information indicating a congestionzone and the travel time through FM multiplex broadcasting and beaconfor a vehicle navigation system installing a digital map database. Thevehicle navigation system receives the vehicle information and displaysa colored congestion zone on a map displayed on a screen and calculatesthe required time to the destination for display.

Thus, to provide the vehicle information, it becomes necessary to passposition information of a road on a digital map. It is also necessary toreport the recommended route and the run locus on a digital map to theassociated party in the service for receiving the information on thecurrent location and the destination and providing information on therecommended route through which the destination will be reached in theshortest time and a vehicle information collection system (probeinformation collection system) for collecting locus information, speedinformation, etc., from a running vehicle (probe car) advanced in studyin recent years.

Hitherto, to report the road position on the digital map, generally thelink numbers assigned to roads and the node numbers determining nodessuch as intersections have been used. However, the node numbers and thelink numbers defined in a road network need to be replaced with newnumbers with new construction or change of a road and the digital mapdata produced by each company must also be updated accordingly and thusthe system using the node numbers and the link numbers involves anenormous social cost for maintenance.

To improve such a point, JP-A-2003-23357 discloses a method of reportingthe road position on the digital map without using the node numbers orthe link numbers and in a small data amount.

In this method, sampling points are again set at given intervals in theroad zone on the digital map to be reported (called “equal-distanceresample”) and compression coding processing is performed for the datastring with the position data of the sampling points arranged in order,and the compressed and coded data is transmitted. At the reception partyreceiving the data, the data string of the position data of the samplingpoints is reconstructed and the road shape is reproduced on the digitalmap of the reception party. Using the position data, positiondetermination and position reference are carried out (map matching) onthe digital map of the reception party for determining the road zone, asrequired.

The compression coding for the data string of the position data isperformed in the order of (1) conversion of position data to a singlevariable, (2) conversion of the value represented by the single variableto a value having a statistical bias, and (3) variable-length coding ofthe provided value as described later:

(1) Conversion of Position Data to a Single Variable

FIG. 26 (a) represents sampling points in a road zone set inequal-distance resample as PJ-1 and PJ. This sampling point (PJ) isuniquely determined by two dimensions of distance (resample length) Lfrom the adjacent sampling point (PJ-1) and angle Θ. Assuming that thedistance is constant (L), the sampling point (PJ) can be represented bythe single variable of only the angle component Θ from the adjacentsampling point (PJ-1). In FIG. 26 (a), as the angle Θ, the angle Θ basedon “absolute azimuth” with the due north azimuth (upper part of thedrawing) as 0 degrees and the magnitude specified clockwise in the rangeof 0 to 360 degrees is shown (absolute azimuth from the due north). Whenxy coordinates (latitude, longitude) of PJ-1 and PJ are (xj−1, yj−1) and(xj, yj), the angle Θ can be calculated according to the followingexpression:Θj−1=tan ⁻¹{(xj−xj−1)/(yj−yj−1)}

Therefore, the road zone can be represented by the data string of theangle components of the sampling points by indicating the constantdistance L between the sampling points and the latitude and longitude ofthe sampling point as the start or the termination (reference point)separately.

(2) Conversion of a Single Variable Value to a Value Having aStatistical Bias

As shown in FIG. 26 (b), the angle component of each sampling point isrepresented by the displacement difference from the angle component ofthe adjacent sampling point, namely, “deflection angle” θj so that thesingle variable values of the sampling point become statistically biasedvalues suited for variable-length coding. The deflection angle θj iscalculated asθj=Θj−Θj−1If the road is linear, the deflection angles θ of the sampling pointsconcentrate on the vicinity of 0 and become data having a statisticalbias.

The angle component of the sampling point can be converted into datahaving a statistical bias by representing the deflection angle θj of anattention sampling point PJ by difference value (predicted differencevalue or predicted error) Δθj from predicted value Sj of the samplingpoint PJ predicted using deflection angles θj−1, θj−2, . . . of thepreceding sampling points PJ-1, PJ-2, . . . as shown in FIG. 26 (c). Thepredicted value Sj, for example, can be defined asSj=θj−1or can be defined asSj=(θj−1+θj−2)/2The predicted difference value Δθj is calculated asΔθj=θj−SjIf the road is curved at a constant curvature, the predicted differencevalues Δθ of the sampling points concentrate on the vicinity of 0 andbecome data having a statistical bias.

FIG. 26 (d) is a graph to show the data occurrence frequency when alinear road zone is displayed as the deflection angle θ and acurvilinear road zone is displayed as the predicted difference value Δθ.The maximum appears at θ (or Δθ)=0° and the occurrence frequency of θand Δθ has a statistical bias.

(3) Variable-Length Coding

Next, the data string values converted into values having a statisticalbias are variable-length coded. Various types of variable-length codingmethod such as a fixed numeric value compression method (0 compression,etc.,), a Shannon-Fano code method, a Huffman code method, an arithmeticcode method, and a dictionary method exist; any method may be used.

Here, the case where the most general Huffman code method is used willbe discussed.

In this variable-length coding, highly frequently occurring data iscoded with a small number of bits and less frequently occurring data iscoded with a large number of bits for reducing the total data amount.The relationship between the data and code is defined based on a codetable.

Now, assume that a list of Δθ at the sampling points of a road zonerepresented in 1° units is

“0_(—)0_(—)−2_(—)0_(—)0_(—)+1_(—)0_(—)0_(—)−1_(—)0_(—)+5_(—)0_(—)0_(—)0_(—)+1_(—)0”

The case where a code table shown in FIG. 27 combining variable-lengthcoding and run-length coding is used to code the data string will bediscussed. The code table defines as follows: Minimum angle resolution(δ) is set to 3° and the representative angle of Δθ in the range of −1°to +1° is 0° and is represented as code “0” and when five successiveoccurrences of 0° exist, they are represented as code “100” when 10successive occurrences of 0° exist, they are represented as code “1101.”The code table also defines as follows: The representative angle of Δθin the range of ±2° to 4° is ±3° and when the value is +, additional bit“0” is added to code “1110” and when the value is −, additional bit “1”is added to code “1110.” The representative angle of Δθ in the range of±5° to 7° is ±6° and additional bit indicating positive or negative isadded to code “111100.” The representative angle of Δθ in the range of±8° to 10° is ±9° and additional bit indicating positive or negative isadded to code “111101.”

Thus, the above-mentioned data string is coded as follows:

“0_(—)0_(—)11101_(—)100_(—)0_(—)0_(—)1111000_(—)100”->“0011101100001111000100”

At the reception party receiving the data, the data string of Δθ isreconstructed using the same code table as that used for coding, andprocessing opposite to that at the transmission party is performed forreproducing the sampling point position data.

The data is thus coded, whereby the data amount of the coded data can bereduced.

JP-A-2003-23357 mentioned above proposes a method of setting distance L2of equal-distance resample short in a zone B where the curvature of theroad shape is large and setting distance L1 of equal-distance resamplelong in a linear zone A with a small curvature, as shown in FIG. 28. Thereason is that if a largely curved road with a large curvature isresampled at a long distance, it becomes impossible to place a samplingpoint at a position indicating the characteristic road shape, thereproducibility of the road shape at the reception party worsens, andthe possibility that erroneous matching may occur becomes high.

Thus, the value that can be taken by resample length Lj in each zone j(quantization resample length) is preset to, for example,40/80/160/320/640/1280/2560/5120 meters, Lj is found according to thefollowing expression using curvature radius ρj of the zone j, and thequantization resample length closest to the value is determined theresample length Lj:Lj=ρj·Kr (where Kr is a fixed parameter)

The method disclosed in JP-A-2003-23357 mentioned above was tried usingthe following three prediction expressions:

Prediction expression 1: Sj=0: Deflection angle is used as it is(substantially prediction is not conducted)

Prediction expression 2: Sj=θj−1: Deflection angle of the preceding nodeis used

Prediction expression 3: Sj=(θj−1+θj−2)/2: Deflection angle averagevalue of the preceding and preceding preceding nodes is used

Consequently, the compression efficiency of prediction expression 1 washigh on average, but to examine the target roads separately, thecompression efficiency of prediction expression 2 or 3 was high in someroads.

Specifically, often prediction expression 2 or 3 was suited in roadsincluding a large number of long and gentle curves, such as an expresshighway; often prediction expression 1 was suited in ordinary roads.

To make a comparison between prediction expressions 2 and 3 ofprediction expressions of the same kind, often prediction expression 3compared a little favorably with prediction expression 2 in compressionefficiency.

It is an object of the invention to provide a coded data generationmethod of efficiently compressing data and generating coded data of aroad shape, etc., on a digital map and provide an apparatus forgenerating the coded data and decoding the coded data.

Patent document 1: JP-A-2003-23357

DISCLOSURE OF THE INVENTION

As a result of considering the above-described points, the followingresults were introduced:

-   In a “gentle curve” portion of an express highway, etc., a    comparatively small curvature becomes almost constant over a long    distance. Thus, focusing attention on one small zone, the curvature    of the part can be very easily predicted from the upstream zone    curvature. Thus, prediction expression 2 or 3 for predicting the    deflection angle of the zone using the deflection angle of the    preceding or preceding preceding node is suited.-   Particularly, prediction expression 3 is prediction using the    average curvature of the upstream preceding and preceding preceding    zones, and the curvature error for each zone is smoothed. Therefore,    highly accurate prediction is made possible in a “gentle curve over    a long distance” as mentioned above.

However, if the curve zone is comparatively short, as compared withprediction expression 2, prediction expression 3 receives the effect ofan upstream longer zone and thus prediction on the periphery of thestart or end part of the curve (=periphery of the part where thecurvature changes) easily fails. As the effect is received, if the curvezone is comparatively short, prediction expression 2 compares favorablywith prediction expression 3 in some cases.

-   On the other hand, in ordinary roads, of ten the road is curved at    right angle in an intersection, etc., or if a curve exists, a    comparatively large curvature is applied only at a short distance.    This means that it is difficult to predict the curvature of one    small zone from the curvature of the upstream preceding zone and if    the curvature of one small zone is predicted, the prediction easily    fails. In such a case, prediction expression 1 of substantially    conducting no prediction (using the deflection angle intact) is    suited.

Taking a survey of the whole roads from the try results described above,it was seen that generally the efficiency of prediction expression 1(using the deflection angle intact without conducting prediction) isgood, but prediction expression 2 or 3 (for predicting the curvature ofthe corresponding point from the upstream curvature) is suited in someroads focusing attention on the discrete roads or road zones.

Based on the points described above, in the invention, in a coded datageneration method of resampling a linear object having a linear shapefor setting a plurality of nodes, arranging position information of eachnode represented by a deflection angle from the immediately precedingnode to generate a data string of the deflection angles, converting thedeflection angles into predicted difference values each indicating thedifference from the predicted value, and variable length coding the datastring of the predicted difference values, a prediction expression tocalculate the predicted value is selected from among a plurality ofprediction expressions.

Thus, the prediction expression to calculate the predicted value isselected dynamically, whereby the effectiveness of data compression canbe enhanced. Particularly, in the described configuration, the datastring of the predicted difference values when the deflection angles areconverted into the predicted difference values is evaluated and aprediction expression is selected based on the evaluation result.

The coded data generation method described above can include thefollowing steps (1) to (6):

(1) Step of resampling a linear object for setting a plurality of nodes.

(2) Step of arranging position data of each node represented by adeflection angle from the immediately preceding node to generate a datastring of the deflection angles.

(3) Step of providing a plurality of prediction expressions to calculatea predicted value of the position data of each of the nodes based on thedata string of the deflection angles.

(4) Step of calculating the predicted value using a predeterminedprediction expression of the plurality of prediction expressions.

(5) Step of converting the data string of the deflection angles into adata string of predicted difference values each indicating thedifference from the calculated predicted value.

(6) Step of variable-length coding the data string of the predicteddifference values to provide the coded data.

The coded data generation method can further include the steps ofacquiring the data strings of the predicted difference valuescorresponding to the plurality of prediction expressions for each of theplurality of prediction expressions according to the step (5);evaluating the data strings of the predicted difference values; andselecting the predetermined prediction expression in the step (4) fromamong the plurality of prediction expressions based on the evaluationresult of the evaluating step.

In the coded data generation method of the invention, the plurality ofprediction expressions contain a prediction expression with 0 as apredicted value.

Usually, the prediction expression can be used to provide efficient datacompression.

The plurality of prediction expressions can contain at least oneprediction expression implemented as a function using at least onedeflection angle preceding an attention deflection angle as a parameter.

In the coded data generation method of the invention, the plurality ofprediction expressions contain a prediction expression with thedeflection angle in the immediately preceding node as a predicted value.

If the prediction expression is used on the periphery of a gentle curve,efficient data compression can be provided.

In the coded data generation method of the invention, the plurality ofprediction expressions contain a prediction expression with the averageor the weighted average of a plurality of preceding deflection angles asa predicted value.

If the prediction expression is used on the periphery of a gentle curve,efficient data compression can be provided.

In the coded data generation method of the invention, the plurality ofprediction expressions contain a prediction expression with the angleresulting from inverting the positive or negative sign of the deflectionangle of the immediately preceding node as a predicted value.

If the resample shape is a zigzag trace, efficient data compression canbe provided by using the prediction expression.

In the coded data generation method of the invention, all deflectionangles contained in the data string of the deflection angles areconverted into predicted difference values, the data string of thepredicted difference values is evaluated, and a prediction expression toconvert all deflection angles into predicted difference values isselected based on the evaluation result.

The prediction expression can be changed dynamically in the shape dataunits of a linear object for enhancing the data compressioneffectiveness.

In the coded data generation method of the invention, each deflectionangle contained in the data string of the deflection anglescorresponding to a part zone of the linear object is converted into apredicted difference value and the data string of the predicteddifference values is evaluated, and a prediction expression to convertthe deflection angles corresponding to the part zone into predicteddifference values is selected based on the evaluation result.

The prediction expression can be changed dynamically at the midpoint ofthe shape data of the linear object for furthermore enhancing the datacompression effectiveness.

In the coded data generation method of the invention, the data string ofthe deflection angles is classified according to state transitionpatterns and a prediction expression to convert the deflection anglesinto predicted difference values is selected in the pattern units.

To adopt the mode, the prediction expression suited for each pattern canbe selected.

In the coded data generation method of the invention, the data string ofthe deflection angles is classified into blocks each containing apredetermined number of data pieces of the deflection angles and aprediction expression to convert the deflection angles into predicteddifference values is selected for each of the blocks.

To adopt the mode, the selected prediction expression appears in theunits of the given number of data pieces in the coded data, so that itbecomes unnecessary to insert a marker code into the coded data.

In the coded data generation method of the invention, the data string ofthe deflection angles is classified into blocks matching the changepoints of the resample length of the resampling and a predictionexpression to convert the deflection angles into predicted differencevalues is selected for each of the blocks.

The characteristic of the shape data often changes at the change pointof the resample length. Thus, to adopt the mode, the predictionexpression matching the characteristic of the shape data can beselected.

In the coded data generation method of the invention, a predictionexpression to convert the attention deflection angle into a predicteddifference value is selected in response to the evaluation result forthe data string of the predicted difference values of a predeterminednumber of deflection angles preceding the attention deflection angle inthe data string of the deflection angles.

The mode can be realized as both the encoding and decoding parties set arule based on a program.

Here, as many deflection angles as the predetermined number may beconverted into a plurality of data strings of predicted differencevalues using the plurality of selection expressions and only if theevaluation result for the data string of the predicted difference valuesbased on a predetermined selection expression satisfies a predeterminedrequirement, the currently used prediction expression may be changed tothe predetermined prediction expression and then the attentiondeflection angle may be converted into a predicted difference value.

In the attention deflection angle or block, a prediction expression maybe selected with reference to the prediction expression selection statein the deflection angles or blocks preceding and following the attentiondeflection angle or block.

In the attention deflection angle or block, if a prediction expressiondifferent from the prediction expressions adopted in the deflectionangles or blocks preceding and following the attention deflection angleor block is selected, a penalty value may be added to the evaluationvalue of the evaluation criterion of the predicted difference valuestring in the attention deflection angle or block. The penalty value canbe set in response to the occurrence frequency of each predictionexpression.

In the coded data generation method of the invention, the data string ofthe predicted difference values is evaluated according to the number of0s contained in the data string and the prediction expression with thelargest number of 0s is selected.

Thus, the data concentrates on 0 and the data compression effectivenessprovided by variable-length coding is enhanced.

In the coded data generation method of the invention, the data string ofthe predicted difference values is evaluated according to thestatistical value (variance, standard deviation, etc.) of the predicteddifference values contained in the data string and the predictionexpression with the variance or standard deviation becoming the smallestis selected.

Thus, the data is statistically biased and the data compressioneffectiveness provided by variable-length coding is enhanced.

In the coded data generation method of the invention, the evaluationvalue for each predicted difference value is preset in response to theoccurrence frequency of the predicted difference value and the datastring of the predicted difference values is evaluated according to thesum value of the evaluation values the predicted difference valuescontained in the data string.

When the occurrence frequency of the predicted difference value is theevaluation value, the prediction expression with the largest sum valueis selected.

When the code length of the predicted difference value is the evaluationvalue, the prediction expression with the smallest sum value isselected.

In the invention, a coded data generation apparatus includes a shapedata resample processing section for resampling a linear object forsetting a plurality of nodes and arranging position data of each noderepresented by a deflection angle from the immediately preceding node togenerate a data string of the deflection angles; a prediction expressiondetermination section, when the data string of the deflection angles isconverted into predicted difference values indicating the differencefrom a predicted value to predict the position data of each of thenodes, for evaluating the data string of the predicted difference valuesand selecting a prediction expression to calculate the predicted valuefrom among a plurality of prediction expressions based on the evaluationresult; and a variable-length coding processing section for convertingeach deflection angle contained in the data string of the deflectionangles generated by the shape data resample processing section into apredicted difference value from the predicted value calculated using theprediction expression determined by the prediction expressiondetermination section and variable-length coding a data string of thepredicted difference values.

The apparatus can carry out the coded data generation method describedabove and can efficiently compress the data amount of the coded data.

In the invention, a coded data reconstruction apparatus includes a codeddata decoding section for decoding variable-length coded datarepresenting position information of a linear object and reproducingshape data containing a data string of difference values each indicatingthe difference between a deflection angle and a predicted value; aprediction expression determination section for determining theprediction expression used to calculate the predicted value from theprovided shape data; and a shape data reconstruction section forcalculating a predicted value using the prediction expression determinedby the prediction expression determination section and reproducingposition information of nodes of the linear object from the data stringof the predicted difference values provided by the coded data decodingsection.

The apparatus can reproduce the position information of the linearobject from the coded data of the position information of the linearobject.

Further, the invention also contains a program for causing a computer toexecute generation of code data provided by coding a linear object. Theprogram causes the computer to execute the steps of resampling a linearobject for setting a plurality of nodes and arranging position data ofeach node represented by a deflection angle from the immediatelypreceding node to generate a data string of the deflection angles; whenthe data string of the deflection angles is converted into predicteddifference values indicating the difference from a predicted value topredict the position data of each of the nodes, evaluating the datastring of the predicted difference values; selecting a predictionexpression to calculate the predicted value from among a plurality ofprediction expressions based on the evaluation result; and convertingeach deflection angle contained in the data string of the deflectionangles generated by a shape data resample processing section into apredicted difference value from the predicted value calculated using thedetermined prediction expression and variable-length coding a datastring of the predicted difference values.

Further, the invention also contains a program for causing a computer todecode code data representing a linear object. The program causes thecomputer to execute the steps of decoding variable length coded datarepresenting position information of a linear object and reproducingshape data containing a data string of difference values each indicatingthe difference between a deflection angle and a predicted value;determining the prediction expression used to calculate the predictedvalue from the provided shape data; and calculating a predicted valueusing the determined prediction expression and reproducing positioninformation of nodes of the linear object from the provided data stringof the predicted difference values. To generate coded data, the codeddata generation method of the invention can efficiently compress data.

The apparatus of the invention can carry out the coded data generationmethod and can effectively compress and code the shape data of a linearobject and can reconstruct the original shape data from the coded data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing to schematically describe a coded data generationmethod in an embodiment of the invention.

FIG. 2 is a drawing to show deflection angle strings of road shape data.

FIG. 3 is a drawing to describe a zigzag phenomenon.

FIG. 4 is a flowchart to show a procedure of a coded data generationmethod in an object road unit selection mode in the embodiment of theinvention.

FIG. 5 is a flowchart to show a resampling and deflection angle stringgeneration procedure in the coded data generation method in theembodiment of the invention.

FIG. 6 is a flowchart to show an evaluation value calculation procedurein the coded data generation method in the embodiment of the invention.

FIG. 7 shows an example of a Huffman table describing occurrencefrequencies.

FIG. 8 shows a data format example of coded data generated in the objectroad unit selection mode in the embodiment of the invention.

FIG. 9 is a flowchart to show a decoding procedure of coded datagenerated in the object road unit selection mode in the embodiment ofthe invention.

FIG. 10 is a flowchart to show a procedure of a coded data generationmethod in a pattern unit selection mode in the embodiment of theinvention.

FIG. 11 shows a data format example of coded data generated in thepattern unit selection mode in the embodiment of the invention.

FIG. 12 is a flowchart to show a decoding procedure of coded datagenerated in the pattern unit selection mode in the embodiment of theinvention.

FIG. 13 is a flowchart to show a procedure of a coded data generationmethod in a block unit selection mode in the embodiment of theinvention.

FIG. 14 shows a data format example of coded data generated in the blockunit selection mode in the embodiment of the invention.

FIG. 15 is a flowchart to show a decoding procedure of coded datagenerated in the block unit selection mode in the embodiment of theinvention.

FIG. 16 is a flowchart to show a procedure of a coded data generationmethod in a resample length linkage mode in the embodiment of theinvention.

FIG. 17 shows a data format example of coded data generated in theresample length linkage mode in the embodiment of the invention.

FIG. 18 is a flowchart to show a decoding procedure of coded datagenerated in the resample length linkage mode in the embodiment of theinvention.

FIG. 19 is a flowchart to show a procedure of a coded data generationmethod in a sequential selection mode in the embodiment of theinvention.

FIG. 20 shows a data format example of coded data generated in thesequential selection mode in the embodiment of the invention.

FIG. 21 is a flowchart to show a decoding procedure of coded datagenerated in the sequential selection mode in the embodiment of theinvention.

FIG. 22 is a drawing to show a total evaluation method of the evaluationvalues provided by prediction expressions and the change penaltyinvolved in changing the prediction expression.

FIG. 23 is a drawing to show a method of dynamically varying the changepenalty involved in changing the prediction expression.

FIG. 24 is a block diagram to show the configurations of an informationtransmission apparatus and an information utilization apparatus in theembodiment of the invention.

FIG. 25 is a block diagram to show the configurations of a probe carinstalled machine and a probe information collection center in theembodiment of the invention.

FIG. 26 is a drawing to describe a method of converting position datainto data having a statistical bias.

FIG. 27 is a drawing to show a code table used for variable-lengthcoding.

FIG. 28 is a drawing to describe sample length change depending on thecurvature of a road shape.

BEST MODE FOR CARRYING OUT THE INVENTION

In a coded data generation method in an embodiment of the invention, tocode a road shape on a digital map of one example of a linear object andgenerate coded data, the coded data is generated roughly in the orderof:

-   (a) resampling of the road shape at the resample length responsive    to the curvature radius of the road-   (b) conversion of position data at sampling point (node) to    deflection angle θ-   (c) conversion of the position data represented by the deflection    angle θ to a value having a statistical bias-   (d) variable-length coding of the provided value.

Resampling of the road shape in (a) is executed according to the methoddescribed in JP-A-2003-23357 and the linear object is resampled forsetting a plurality of nodes. For the conversion to deflection angle θin (b), the position data of each node is represented as the anglecomponent according to the method described in JP-A-2003-23357 and thenthe angle component is converted into the deflection angle θ. FIGS. 2(a), (b), and (c) show (deflection angle) data strings representing theposition data in the nodes of the shape data of object roads A, B, and C(which will be hereinafter referred to as “deflection angle strings”).Each row is made up of 10 pieces of data, and the data numbers are shownat the left end. The numeral enclosed in < > in the deflection anglestring is the resample length change code indicating the quantizationcode of the resample length, and the numeric values to the right of thecode are representations each of the position data of each node providedby resampling at the resample length of the code as the deflection angleθ (deg). The position data of each node is represented as the deflectionangle from the immediately preceding node, and the pieces of theposition data are collected to generate the deflection angle datastring.

The conversion of the position data to a value having a statistical biasin (c) is processing of adaptively using a calculation expression of apredicted value Sj (prediction expression) for predicting the value ofposition data to calculate Sj and converting the deflection angle θ inthe deflection angle string into the difference value from the predictedvalue Sj (predicted difference value).

The variable-length coding in (d) is processing of variable-lengthcoding of the predicted difference value (predicted error) of the shapedata converted into predicted difference value string and is performedaccording to the method described in JP-A-2003-23357.

Therefore, the coded data generation method is characterized by theposition data conversion processing in (c).

In the position data conversion processing in (c), any of the followingfour prediction expressions (φ=1, φ=2, φ=3, φ=4) is used to predict thepredicted value Sj. The expressions are provided and are recorded inpredetermined memory, etc.

φ=1; Prediction Expression Sj=0

That is, the position data of each node j is represented as thedeflection angle θj, and the predicted value is 0.

φ=2; Prediction Expression Sj=θj−1

That is, the position data of each node j is represented as (θj−θj−1).

φ=3; Prediction Expression Sj=(θj−1+θj−2)/2

That is, the position data of each node j is represented as{θj−(θj−1+θj−2)/2}.

φ=4; Prediction Expression Sj=−θj−1

That is, the position data of each node j is represented as (θj+θj−1).

If φ=2 or φ=3 is used when the position data of each underscored curvepart in the deflection angle string in FIG. 2, high compressionefficiency is provided.

φ=2, φ=3, φ=4 is implemented as a function using at least one deflectionangle preceding the attention deflection angle as a parameter.

In φ=2, the deflection angle immediately preceding the attentiondeflection angle is used as the predicted value.

In φ=3, the average of the two deflection angles preceding the attentiondeflection angle is used as the predicted value. However, the number ofthe deflection angles preceding the attention deflection angle isarbitrary, and a weighted average like (aθj−1+bθj−2)/(a+b) may be usedas the predicted value (a and b are each a real number larger than 0).

In φ=4, the angle of the deflection angle immediately preceding theattention deflection angle with the sign provided by inverting thepositive or negative sign of the deflection angle immediately precedingthe attention deflection angle is used as the predicted value.

As shown in FIG. 3, the resample shape (solid line) may show a zigzag inthe bend part of the road shape (dotted line); if φ=4 is used when theposition data of such a part is converted, high compression efficiencyis provided. If the angle resolution δat the resampling time is set andthe road shape is traced, the angles that can be used are limited andthus the zigzag phenomenon inevitably occurs (inevitability involved inangle quantization). In the deflection angle string in FIG. 2 (a), theposition data of the parts where a zigzag phenomenon occurs aredisplayed as italic characters.

If φ=1 is used when the road shape is close to a line (containing thecase where if a curve exists, it immediately reaches the end and thecase where the road is almost a line and the angle is occasionallycorrected), high compression efficiency is provided. To convert theposition data of a usual road shape, φ=1 is used.

The prediction expression of the predicted value Sj used for conversionprocessing of the deflection angle θ is selected dynamically in responseto the road shape. To select the prediction expression,

-   (1) a mode of dynamically selecting a prediction expression for each    object road (called “object road unit selection mode”)-   (2) a mode of detecting the pattern of the deflection angle string    of the object road and selecting a prediction expression in the    pattern unit (called “pattern unit selection mode”)-   (3) a mode of dividing the deflection angle string of the object    road into blocks each made up of a given number of data pieces and    selecting a prediction expression in the block unit (called “block    unit selection mode”)-   (4) a mode of selecting a prediction expression each time the    resample length change code is changed in the deflection angle    string of the object road (called “resample length linkage mode”)-   (5) a mode of selecting a prediction expression in accordance with a    program rule from the situation in the N pieces of position data    upstream from the attention position data in the deflection angle    string of the object road (called “sequential selection mode”)    and the like are considered. Which mode is to be used needs to be    predetermined between the transmission party and the reception    party.

FIG. 1 schematically shows the relationship between each predictionexpression selected in the resample length linkage mode and the roadshape. The road shape is indicated by the dotted line, the resampleshape is indicated by the solid line, and the resample length changepoints are indicated by <M1>, <M2>, and <M3>. φ=1 is selected in range Ain which the road shape is linear; φ=4 is selected in range B in which azigzag phenomenon occurs; φ=2 or φ=3 is selected in curve range C; andφ=1 is selected in range D in which the road shape is undetermined.

Next, the processing in each mode will be discussed in detail.

(1) Object Road Unit Selection Mode

A flowchart of FIG. 4 shows a processing procedure in the object roadunit selection mode. The shape data of the object road is acquired froma digital map database (step 1), and the position data of each nodegenerated in resample is represented as the deflection angle θ and adeflection angle string is generated (step 2).

The processing at step 2 is performed according to a procedure shown inFIG. 5 in detail. That is, the angle resolution δ at each deflectionangle of each resample length is preset (step 21), the shape data of theobject road is converted into a curvature function (step 22), and theresample length L of each zone is determined from the curvature (step23). Next, the object road is resampled according to the representativeangle of the angle resolution δresponsive to the resample length L andthe deflection angle (step 24), and the shape data of the object road isconverted into a deflection angle string of a list of resample zonelength change code and deflection angle quantization values (step 25).

Upon completion of generation of the deflection angle string, theprediction expressions φ=1, φ=2, φ=3, and φ=4 are applied to the wholedeflection angle string of the object road, the deflection angle θ inthe deflection angle string is converted into the difference value fromthe predicted value Sj (predicted difference value), and whichprediction expression is optimum is determined (step 3). The processingat step 3 is performed according to a procedure shown in FIG. 6 indetail. That is, the deflection angle string to be evaluated (in theobject road unit selection mode, the whole deflection angle string ofthe object road) is acquired (step 31), the prediction expressions areused in order starting at φ=1 (step 32), the predicted value Sj iscalculated according to each prediction expression, the deflection angleθ in the deflection angle string is converted into a predicteddifference value string represented as predicted difference value(=Δθj=θj−Sj) (step 33), and the evaluation value of the deflection anglestring is calculated (step 34).

The evaluation value is calculated as below in (i) to (iii):

-   (i) The number of 0s contained in the predicted difference value    string is used as the evaluation value and higher evaluation is    given to the predicted difference value string having a larger    number of 0s (the prediction expression containing the greatest    number of 0s is selected).-   (ii) The calculation value of the statistical value of data    contained in the predicted difference value string (for example,    variance, standard deviation, etc.) is used as the evaluation value    and higher evaluation is given to the predicted difference value    string having smaller variance or standard deviation.-   (iii) The score responsive to the occurrence frequency is preset in    data appearing in the predicted difference value string, the    cumulative value resulting from adding the scores of the data    appearing in the predicted difference value string to be evaluated    is used as the evaluation value, and evaluation responsive to the    cumulative value is given. In a Huffman table in FIG. 7, the    occurrence frequency (or occurrence probability) of each angle is    described and shorter code is assigned to the angle having the    higher occurrence frequency. To add the value of the corresponding    occurrence frequency in the table in response to the angle appearing    in the predicted difference value string to be evaluated, the    cumulative value is used as the evaluation value and higher    evaluation is given to the predicted difference value string having    the higher cumulative value. To add the code length of the angle in    response to the angle appearing in the predicted difference value    string to be evaluated, the cumulative value is used as the    evaluation value and higher evaluation is given to the predicted    difference value string having the smaller cumulative value. Such a    score table corresponding to the occurrence frequencies is    previously possessed, whereby the evaluation in (iii) is also    possible if variable-length coding other than the Huffman coding is    performed.

All prediction expressions are used to perform the processing at steps33 and 34 and upon completion of the processing (step 35, 36), theprediction expression of the best evaluation value is determined (step37). That is, for each of the prediction expressions φ=1, φ=2, φ=3, andφ=4, the corresponding predicted difference data value string isgenerated and the data strings are evaluated.

The prediction expression of the best evaluation value is thus selectedand the deflection angle θ in the deflection angle string is convertedinto the predicted difference value from the predicted value calculatedaccording to the prediction expression (step 4) and the whole shape dataconverted into the predicted difference value string is compressed byvariable-length coding (step 5). The used prediction expression isdefined for the provided coded data (step 6).

FIG. 8 shows the data format of the coded data generated according tothe object road unit selection mode. Here, the data representing theused prediction expression is inserted before the shape data main bodyof the object road.

A flowchart of FIG. 9 shows a processing procedure of reproducing theshape data of the object road from the coded data. The shape datasubjected to the variable length decoding processing is taken out fromthe coded data (step 41), a header is referenced for determining theprediction expression (step 42), the angle data read from the shape datais converted into the deflection angle according to the predictionexpression (step 43), and the shape data of the object road isreproduced (step 44).

(2) Pattern Unit Selection Mode

A flowchart of FIG. 10 shows a processing procedure in the pattern unitselection mode. Shape data acquiring (step 51) and resample anddeflection angle string conversion processing (step 52) are similar tothose in the object road unit selection mode (FIGS. 4 and 5).

The provided deflection angle string is scanned, a curve pattern(underscore portion in FIG. 2) to which φ=2 or φ=3 is to be appliedwhere P or more data pieces of non-0 having the same positive ornegative sign are successive or a zigzag pattern (italic portion in FIG.2 (a)) to which φ=4 is to be applied where Q or more data pieces of thesame absolute value with an alternating pattern of positive and negativesigns are successive is extracted, and the deflection angle string datais classified into pattern groups and a group not belonging to anypatterns to which φ=1 is to be applied (step 53). The predictionexpression of the best evaluation value is selected for each group andthe deflection angle string is converted into-a-predicted differencevalue string (step 54). At this time, if the best prediction expressioncorresponding to one group is not uniquely determined, a plurality ofprediction expressions are applied to the deflection angle string of thegroup and the prediction expression of the best evaluation value isdetermined according to the procedure in FIG. 6.

Next, the whole shape data converted into the predicted difference valuestring is compressed by variable-length coding (step 55), and the usedprediction expression is defined for the provided coded data for eachgroup (step 56).

In the embodiment, the deflection angle data strings are classified intoblocks (groups) corresponding to state transition patterns of thedeflection angles. The optimum prediction expression is selected foreach block.

FIG. 11 shows the data format of the coded data generated according tothe pattern unit selection mode. Here, the prediction expression initialvalue representing the prediction expression used in the first group isinserted before the shape data main body of the object road, andpreceding the position data of each subsequent group, a predictionexpression marker indicating prediction expression insertion and theprediction expression number of the prediction expression used in thegroup are inserted.

A flowchart of FIG. 12 shows a processing procedure of reproducing theshape data of the object road from the coded data (decoding method ofthe coded data). The shape data subjected to the variable-lengthdecoding processing is taken out from the coded data (step 61), thenumber of the angle data read from the shape data is set to the initialvalue and the first used prediction expression is set to the predictionexpression represented by the prediction expression initial value (step62), the corresponding angle data is read from the shape data (step 63),and whether or not prediction expression change code is inserted beforethe angle data is determined (step 64). If prediction expression changecode is not inserted, the setup prediction expression is used as it is(step 66), and the angle data is converted into the deflection angleaccording to the prediction expression (step 67). If predictionexpression change code is inserted, the prediction expression is changedto a new prediction expression specified by the code (step 65), and theangle data is converted into the deflection angle according to theprediction expression (step 67). Such processing is performed for allangle data (steps 68 and 69) and the shape data of the object road isreproduced (step 70).

In the mode, the data string of the deflection angles corresponding to apart zone of the linear object (road shape) rather than the whole isconverted into a data string of the predicted difference values and theoptimum prediction expression for converting the deflection anglescorresponding to the part zone into the predicted difference values isselected based on the evaluation result of the data string of thepredicted difference values. This philosophy is adopted common to themodes in (3) to (5) described below.

(3) Block Unit Selection Mode

A flowchart of FIG. 13 shows a processing procedure in the block unitselection mode. Shape data acquiring (step 71) and resample anddeflection angle string conversion processing (step 72) are similar tothose in the object road unit selection mode (FIGS. 4 and 5).

For example, one block is made up of 10 pieces of data on one row of thedeflection angle string shown in FIG. 2, the prediction expressions φ=1,φ=2, φ=3, and φ=4 are applied to the deflection angle string in theblock units for converting the deflection angle θ in the deflectionangle string into predicted difference values, and which predictionexpression is optimum is determined (step 73). The evaluation method isthe same as that in the object road unit selection mode (FIG. 6). Theprediction expression of the best evaluation value is selected for eachblock and the deflection angle string is converted into a predicteddifference value string (step 74), the whole shape data converted intothe predicted difference value string is compressed by variable-lengthcoding (step 75), and the used prediction expression is defined for theprovided coded data for each block (step 76).

FIG. 14 shows the data format of the coded data generated according tothe block unit selection mode. Here, the prediction expression initialvalue representing the prediction expression used in the first block isinserted before the shape data main body of the object road, andpreceding the position data of each subsequent block, the predictionexpression number is inserted. Since the insertion position of theprediction expression number is automatically determined by the numberof data pieces contained in the block, a prediction expression markerneed not be inserted.

A flowchart of FIG. 15 shows a processing procedure of reproducing theshape data of the object road from the coded data. The shape datasubjected to the variable-length decoding processing is taken out fromthe coded data (step 81), the number of the block read from the shapedata is set to the initial value and the first used predictionexpression is set to the prediction expression represented by theprediction expression initial value (step 82), the angle data in theblock is read from the shape data (step 83), and the angle data isconverted into the deflection angle according to the predictionexpression defined in the block (step 84). Such processing is performedfor all blocks (steps 85 and 86) and the shape data of the object roadis reproduced (step 87).

In the mode, the predetermined number of data pieces of the deflectionangles contained in one block is 10, but the number can be changed asdesired.

(4) Resample Length Linkage Mode

A flowchart of FIG. 16 shows a processing procedure in the resamplelength linkage mode. Shape data acquiring (step 91) and resample anddeflection angle string conversion processing (step 92) are similar tothose in the object road unit selection mode (FIGS. 4 and 5).

In the deflection angle string shown in FIG. 2, the angle data existingbetween one resample length change code and the next resample lengthchange code (two resample length change points), resampled at the sameresample length is one block (block between the numerals enclosed in< >), the prediction expressions φ=1, φ=2, φ=3, and φ=4 are applied tothe deflection angle string in the block units for converting thedeflection angle θ in the deflection angle string into predicteddifference values, and which prediction expression is optimum isdetermined (step 93). The evaluation method is the same as that in theobject road unit selection mode (FIG. 6). The prediction expression ofthe best evaluation value is selected for each block and the deflectionangle string is converted into a predicted difference value string (step94), the whole shape data converted into the predicted difference valuestring is compressed by variable-length coding (step 95), and the usedprediction expression is defined for the provided coded data for eachblock (step 96).

FIG. 17 shows the data format of the coded data generated according tothe resample length linkage mode. Here, the prediction expressioninitial value representing the prediction expression used in the firstblock is inserted before the shape data main body of the object road,and the prediction expression number of the prediction expression usedin each subsequent block is defined in zone length information followinga zone length marker, inserted into the start position of the angle dataresampled at the same resample length.

A flowchart of FIG. 18 shows a processing procedure of reproducing theshape data of the object road from the coded data. The shape datasubjected to the variable-length decoding processing is taken out fromthe coded data (step 101), the number of the block of the same resamplelength read from the shape data is set to the initial value (step 102),all angle data contained in the block with the same resample length andthe prediction expression associated with the resample length changecode are acquired (step 103), and the angle data in the block isconverted into the deflection angle according to the predictionexpression (step 104). Such processing is performed for all blocks eachwith the same resample length (steps 105 and 106) and the shape data ofthe object road is reproduced (step 107).

(5) Sequential Selection Mode

A flowchart of FIG. 19 shows a processing procedure in the sequentialselection mode. Shape data acquiring (step 111) and resample anddeflection angle string conversion processing (step 112) are similar tothose in the object road unit selection mode (FIGS. 4 and 5).

The number of angle data extracted from a deflection angle string is setto initial value and the first used prediction expression is set to φ=1(step 113). The corresponding angle data and a predetermined number of(here, P) samples upstream from the angle data are extracted from thedeflection angle string (step 114), the prediction expressions φ=1, φ=2,φ=3, and φ=4 are applied to the deflection angle string made up of theextracted data for converting the deflection angle θ in the deflectionangle string into predicted difference values, and prediction expressionof the best evaluation value φnew is selected (step 115). The evaluationmethod is the same as that in the object road unit selection mode (FIG.6).

Based on the evaluation result, whether or not the already setupprediction expression needs to be changed is determined according to theprediction expression change condition (step 116). That is, in the mode,while the upstream shape of the position data to be coded is referenced,whether or not the optimum prediction expression used for the positiondata is to be changed is determined.

As the change condition, for example, the following (i) or (iii)condition is set:

(i) If evaluation value difference Δ between the currently usedprediction expression φ and the prediction expression φnew is greaterthan a predetermined value, the prediction expression is changed.

(ii) If a state in which higher evaluation is given to the predictionexpression φnew rather than the currently used prediction expression φcontinues Q times or more, the prediction expression is changed.

If the change condition is not satisfied, the already setup predictionexpression is used as it is (step 118), and the angle data is convertedinto predicted difference value (step 119). If the change condition issatisfied, the prediction expression is changed to the predictionexpression φnew (step 117), and the angle data is converted intopredicted difference value (step 119). Such processing is performed forall angle data (steps 120 and 121) and the whole shape data convertedinto the predicted difference value string is compressed byvariable-length coding (step 122).

The prediction expression change rule is determined in the programdefining the coding processing.

In the embodiment, a prediction expression for converting the attentiondeflection angle into the predicted difference value is selected inresponse to the evaluation result for the data string of the predicteddifference values of the predetermined number of (P) deflection anglespreceding the attention deflection angle. Further, only if theevaluation result satisfies the predetermined requirement, the currentlyused prediction expression is changed to a predetermined predictionexpression.

FIG. 20 shows the data format of the coded data generated according tothe sequential selection mode. Since the coded data does not containinformation specifying a prediction expression, the data amount is alsosmall. The prediction expression used in decoding is selected based onthe rule of the program defining the decoding of the coded data.

A flowchart of FIG. 21 shows a processing procedure of reproducing theshape data of the object road from the coded data. The shape datasubjected to the variable-length decoding processing is taken out fromthe coded data (step 131), the number of the angle data read from theshape data is set to the initial value, and the first used predictionexpression is set to φ=1 (step 132). The corresponding angle data andthe P samples upstream from the angle data are read (step 133), theprediction expressions φ=1, φ=2, φ=3, and φ=4 are applied to thedeflection angle string made up of the read data for converting thedeflection angle θ in the deflection angle string into predicteddifference values, and prediction expression of the best evaluationvalue φnew is selected (step 134). Next, based on the evaluation result,whether or not the already setup prediction expression needs to bechanged is determined according to the prediction expression changecondition (step 135). If the change condition is not satisfied, thealready setup prediction expression is used as it is (step 137), and theangle data is converted into a deflection angle according to theprediction expression (step 138). If the change condition is satisfied,the prediction expression is changed to the prediction expression φnew(step 136), and the angle data is converted into a deflection angleaccording to the prediction expression (step 138). Such processing isperformed for all angle data (steps 139 and 140) and the shape data ofthe object road is reproduced (step 141).

Thus, to generate the coded data of the road shape, the predictionexpression used for calculating the predicted value to convert theposition data into a predicted difference value is selected adaptivelyfor each road or for each road portion, whereby the data amount of thecoded data can be compressed efficiently.

By the way, in the modes in (1) to (4) described above, to change theprediction expression in one shape data, it becomes necessary toincorporate information of “explicitly describing prediction expressionchange and specifying the prediction expression ID” in data. This canresult in one factor of an increase in the data amount. Further, forexample, when “although generally prediction expression 1 is good in thewhole road zone, another prediction expression is good instantaneously,”it is not much desirable to frequently change the prediction expression.To at least change the prediction expression, it is desirable that “thedata amount reduction effect provided by adopting a new predictionexpression” should be over “the loss involved in changing the predictionexpression.”

Then, it is considered that “change penalty” is added to “the evaluationvalue provided by each prediction expression” and “whether or not theprediction expression is to be changed” is determined according to theevaluation result. In other words, it is considered that the predictionexpression change is provided with hysteresis.

FIG. 22 is a drawing to show a total evaluation method of the evaluationvalues provided by the prediction expressions and the change penaltyinvolved in changing the prediction expression.

In FIG. 22 (a), as the prediction expression is changed, the data amountcan be reduced and the data transmission efficiency can be enhanced andtherefore the prediction expression is changed. On the other hand, inFIG. 22 (b), as the prediction expression is not changed, goodtransmission efficiency is provided and therefore the predictionexpression is not changed.

If the numbers of data classification blocks and deflection anglesincrease, a combination optimization problem such that “what combinationis most efficient?” occurs. As the numbers of blocks and deflectionangles increase, it becomes difficult to find out an optimal solutioneasily and rapidly.

Then, as a method of finding out a quasi-optimal solution rapidly, thefollowing two modes are possible:

(i) The change penalty is made relatively small for a highly frequentlyoccurring prediction expression and is made large for a less frequentlyoccurring prediction expression. If a less frequently occurringprediction expression is adopted, the possibility that the predictionexpression may be changed to another prediction expression in the nextblock is large and therefore change to the less frequently occurringprediction expression is suppressed.

(ii) Usually, processing is performed in order in one direction and thusthe downstream situation is estimated from the upstream result of theattention block and the change penalty is changed dynamically.

Further, it is considered that the optimum prediction expressionoccurrence frequency changes according to the compression rate. Forexample, if the compression rate is raised, there is a trend for theregularity of the deflection angles to disappear and for φ=1 to becomean optimum expression. In contrast, if the compression rate is lowered,there is a trend to enhance the regularity of the deflection angles andfor φ=2 or φ=3 to become an optimum expression.

Therefore, in (i), it is considered that a method of “once calculatingthe evaluation value of each prediction expression for the whole shapedata and determining the change penalty in response to the evaluationvalue” is good.

FIG. 23 is a drawing to show a method of dynamically varying the changepenalty involved in changing the prediction expression.

FIG. 24 shows the configurations of an information transmissionapparatus (coded data generation apparatus) 20 for executing the codeddata generation method to report the vehicle information object road andan information utilization apparatus (coded data reconstructionapparatus) 40 such as a vehicle-installed navigation system or apersonal computer to make the most of the provided vehicle information.The information transmission apparatus 20 includes an event informationinput section 21 to which congestion information and traffic accidentinformation are input, a shape data extraction section 23 for extractingthe road shape data in the object road zone of vehicle information froma digital map database A 22, a shape data resample processing section 26for resampling the road shape data extracted in the shape dataextraction section 23 and generating a deflection angle string of nodeposition data, a prediction expression determination section 25 fordetermining a prediction expression to convert the deflection anglestring into a predicted difference value string, a variable-lengthcoding processing section 28 for converting the deflection angle of theshape data into a predicted difference value using the predictionexpression determined by the prediction expression determination section25 and performing compression and coding, a compressed data storagesection 27 for storing the compressed and coded road shape data andproviding the stored data for an external medium, and a shape datatransmission section 29 for transmitting the compressed and coded roadshape data.

On the other hand, the information utilization apparatus 40 includes ashape data reception section 41 for receiving the provided road shapedata, a coded data decoding section 42 for decoding the compressed andcoded data, a prediction expression determination section 47 foridentifying the prediction expression used at the conversion time to thepredicted difference value, a shape data reconstruction section 43 forreconstructing the shape data using the prediction expression identifiedby the prediction expression determination section 47, a map matchingsection 45 for performing map matching using data in a digital mapdatabase B 46 and determining a road zone represented by node points ona digital map, and an information utilization section 44 for making themost of the provided information.

In the information transmission apparatus 20, the shape data extractionsection 23 extracts the road shape data of the object road, and theshape data resample processing section 26 resamples the road shape dataand generates a deflection angle string of the road shape data. Theprediction expression determination section 25 determines the predictionexpression to convert the deflection angle string into a predicteddifference value string according to the “object road unit selectionmode,” the “pattern unit selection mode,” the “block unit selectionmode,” the “resample length linkage mode,” or the “sequential selectionmode” described above. The variable-length coding processing section 28calculates a predicted value according to the prediction expressiondetermined by the prediction expression determination section 25,subtracts the predicted value from each deflection angle in thedeflection angle string to generate a predicted difference value string,and variable-length codes the predicted difference value string.

The road shape data compressed by variable-length coding is recorded onan external medium and is provided, or is transmitted from the shapedata transmission section 29.

In the information utilization apparatus 40 receiving the road shapedata, the coded data decoding section 42 decodes the compressed andcoded data. The prediction expression determination section 47identifies the prediction expression to decode the deflection angle fromthe provided data, and the shape data reconstruction section 43reproduces the deflection angle string using the prediction expressionand converts each deflection angle into latitude and longitude data toreproduce the road shape data. The resample shape concatenating thenodes reproduced is superposed on the digital map for display on ascreen of the information utilization apparatus 40.

To accurately determine the road zone, the map matching section 45performs map matching between the position data of the node pointreproduced and the map data in the digital map database B 46 anddetermines the object road on the digital map data. The informationutilization apparatus 40 can also implement a car navigation receiver ora map display terminal.

FIG. 25 shows the configurations of a probe car installed machine (codeddata generation apparatus) 60 for executing the coded data generationmethod to report the run locus and a probe information collection center(coded data reconstruction apparatus) 50 for collecting probeinformation. The probe car installed machine 60 includes a home vehicleposition determination section 61 for determining the home vehicleposition based on information received from a GPS antenna 73 anddetection information of a gyro 74, a digital map database 69, a runlocus storage section 62 for storing the run locus of the home vehicle,a run locus shape resample processing section 63 for resampling the runlocus and generating a deflection angle string of node position data, aprediction expression determination section 68 for determining aprediction expression to convert the deflection angle string into apredicted difference value string, a variable-length coding processingsection 64 for converting the deflection angle of the run locus shapedata into a predicted difference value using the prediction expressiondetermined by the prediction expression determination section 68 andperforming compression and coding, a compressed data storage section 65for storing the compressed and coded run locus shape data, and a runlocus transmission section 66 for transmitting the compressed and codedrun locus shape data.

On the other hand, the probe information collection center 50 includes arun locus reception section 51 for receiving the run locus shape dataprovided by the probe car installed machine 60, a coded data decodingsection 52 for decoding the compressed and coded reception data, aprediction expression determination section 55 for identifying theprediction expression used at the conversion time to the predicteddifference value, a run locus shape reconstruction section 53 forreconstructing the run locus shape using the prediction expressionidentified by the prediction expression determination section 55, and arun locus and measurement information utilization section 54 for makingthe most of the run locus and measurement information collected from theprobe car installed machine 60 to generate vehicle information.

The home vehicle position detected in the home vehicle positiondetermination section 61 is stored in the run locus storage section 62of the probe car installed machine 60 in sequence as the run locus. Therun locus shape resample processing section 63 reads the run locus datastored in the run locus storage section 62, resamples the run locus, andgenerates a deflection angle string of the run locus shape data. Theprediction expression determination section 68 determines the predictionexpression to convert the deflection angle string into a predicteddifference value string according to the “object road unit selectionmode,” the “pattern unit selection mode,” the “block unit selectionmode,” the “resample length linkage mode,” or the “sequential selectionmode” described above. The variable-length coding processing section 64calculates a predicted value according to the prediction expressiondetermined by the prediction expression determination section 68,subtracts the predicted value from each deflection angle in thedeflection angle string to generate a predicted difference value string,and variable-length codes the predicted difference value string. Thecompressed and coded data is transmitted to the probe informationcollection center 50 at the probe information transmission timing. Thedata may be stored on an external medium so as to be provided for theprobe information collection center 50.

In the probe information collection center 50, the coded data decodingsection 52 decodes the data collected from the probe car installedmachine 60. The prediction expression determination section 55identifies the prediction expression to decode the deflection angle fromthe provided data, and the run locus shape reconstruction section 53reproduces the deflection angle string using the prediction expressionand converts each deflection angle into latitude and longitude data toreproduce the run locus data. The most of the run locus information ismade to generate vehicle information together with the measurementinformation of the speed, etc., measured in the probe car installedmachine 60.

Thus, the information transmission apparatus and the probe car installedmachine can efficiently compress the data amount by generating the codeddata of the object road and the run locus using the coded datageneration method of the invention.

A probe car system is constructed using the probe car installed machine60 and the probe information collection center 50 in combination, and aninformation transmission method in the probe car system is accomplishedbetween them; this method is accomplished using the coded datageneration method and the coded data decoding method of the invention incombination.

In the example described above, the coded data generation apparatus isthe information transmission apparatus 20 or the probe car installedmachine 60 of an information transmission center; it is an embodiment inthe information transmission party and may be any if it is an apparatusor terminal that can transmit information. Further, the generated codeddata can also be recorded on a medium so as to be provided for any otherapparatus. The information utilization apparatus 40 and the probeinformation collection center 50 of coded data reconstruction apparatusare also examples and may be any apparatus if the apparatus can make themost of information, such as a person computer or a mobile terminal. Ofcourse, similar advantages can also be provided in the informationcollection center or the apparatus in the center that can reconstructthe coded data. Further, similar advantages can also be provided byperforming reconstruction processing using a medium, etc., recording thecoded data, needless to say.

The invention also contains a program for causing a computer to executegeneration of code data provided by coding a linear object. The programcauses the computer to execute the steps of resampling a linear objectfor setting a plurality of nodes and arranging position data of eachnode represented by a deflection angle from the immediately precedingnode to generate a data string of the deflection angles; when the datastring of the deflection angles is converted into predicted differencevalues indicating the difference from a predicted value to predict theposition data of each of the nodes, evaluating the data string of thepredicted difference values; selecting a prediction expression tocalculate the predicted value from among a plurality of predictionexpressions based on the evaluation result; and converting eachdeflection angle contained in the data string of the deflection anglesgenerated by a shape data resample processing section into a predicteddifference value from the predicted value calculated using thedetermined prediction expression and variable-length coding a datastring of the predicted difference values. Such a program isincorporated in the information transmission apparatus 20 and the probecar installed machine 60 in various formats. For example, the programcan be recorded in predetermined memory in the information transmissionapparatus 20, the probe car installed machine 60 or an externalapparatus. The program may be recorded in an information record unitsuch as a hard disk and an information record medium such as a CD-ROM, aDVD-ROM, or a memory card. The program may be downloaded via a network.

Further, the invention also contains a program for causing a computer todecode code data representing a linear object. The program causes thecomputer to execute the steps of decoding variable-length coded datarepresenting position information of a linear object and reproducingshape data containing a data string of difference values each indicatingthe difference between a deflection angle and a predicted value;determining the prediction expression used to calculate the predictedvalue from the provided shape data; and calculating a predicted valueusing the determined prediction expression and reproducing positioninformation of nodes of the linear object from the provided data stringof the predicted difference values.

Such a program is also incorporated in the information utilizationapparatus 40 and the probe information collection center 50 in variousformats. For example, the program can be recorded in predeterminedmemory in the information utilization apparatus 40, the probeinformation collection center 50 or an external apparatus. The programmay be recorded in an information record unit such as a hard disk and aninformation record medium such as a CD-ROM, a DVD-ROM, or a memory card.The program may be downloaded via a network.

The information transmission apparatus 20 and the informationutilization apparatus 40 of the invention or the probe car installedmachine 60 and the probe information collection center 50 are used incombination to make up a map data distribution system.

The algorithm (program) complying with the coded data generation methodof the invention can be recorded on a record medium recording the mapdata corresponding to various pieces of map information in the map datamain body. Accordingly, it is made possible to compress and code the mapdata main body.

In the description of the embodiment, the linear object is the roadshape for position reference by way of example. However, the linearobject is not limited to the road shape. The “linear object” containsall elongated shapes including various forms of a line, a curve, etc.,and can contain all geographic information that can be represented bylinear shapes on a map. Further, it also contains all represented bylinear shapes, not relating to a map, such as fingerprints.

While the invention has been described in detail with reference to thespecific embodiment, it will be obvious to those skilled in the art thatvarious changes and modifications can be made without departing from thespirit and the scope of the invention.

The present application is based on Japanese Patent Application (No.2003-357730) filed on Oct. 17, 2003, which is incorporated herein byreference.

INDUSTRIAL APPLICABILITY

The coded data generation method of the invention can be used when codeddata representing position information of road shapes, rivers, railways,administrative district boundaries, contour lines, etc., of a digitalmap is generated and is transmitted, stored, retained, etc. In additionto the digital map, the coded data generation method can also be appliedwhen coded data representing linear objects of various patterns,fingerprints, etc., is generated and is transmitted, stored, retained,etc.

1. A generation method of coded data provided by coding a linear object,said coded data generation method comprising the steps of: (1)resampling the linear object for setting a plurality of nodes; (2)arranging position data of each node represented by a deflection anglefrom the immediately preceding node to generate a data string of thedeflection angles; (3) providing a plurality of prediction expressionsto calculate a predicted value of the position data of each of the nodesbased on the data string of the deflection angles; (4) calculating thepredicted value using a predetermined prediction expression of theplurality of prediction expressions; (5) converting the data string ofthe deflection angles into a data string of predicted difference valueseach indicating the difference from the calculated predicted value; and(6) variable-length coding the data string of the predicted differencevalues to provide the coded data.
 2. The coded data generation method asclaimed in claim 1 further comprising the steps of: acquiring the datastrings of the predicted difference values corresponding to theplurality of prediction expressions for each of the plurality ofprediction expressions according to said step (5); evaluating the datastrings of the predicted difference values; and selecting thepredetermined prediction expression in said step (4) from among theplurality of prediction expressions based on the evaluation result ofthe evaluating step.
 3. The coded data generation method as claimed inclaim 1 wherein the plurality of prediction expressions contain at leastone prediction expression with 0 as a predicted value.
 4. The coded datageneration method as claimed in claim 1 wherein the plurality ofprediction expressions contain at least one prediction expressionimplemented as a function using at least one deflection angle precedingan attention deflection angle as a parameter.
 5. The coded datageneration method as claimed in claim 4 wherein the predictionexpression implemented as the function contains at least one predictionexpression with the deflection angle immediately preceding the attentiondeflection angle as a predicted value.
 6. The coded data generationmethod as claimed in claim 4 wherein the prediction expressionimplemented as the function contains at least one prediction expressionwith an average or a weighted average of a plurality of deflectionangles preceding the attention deflection angle as a predicted value. 7.The coded data generation method as claimed in claim 4 wherein theprediction expression implemented as the function contains at least oneprediction expression with the angle resulting from inverting thepositive or negative sign of the deflection angle of the nodeimmediately preceding the attention deflection angle as a predictedvalue.
 8. The coded data generation method as claimed in claim 2 whereinin said step (5), the data string of the deflection angles correspondingto a part zone of the linear object is converted into a data string ofthe predicted difference values, and a prediction expression to convertthe deflection angles corresponding to the part zone into predicteddifference values is selected based on the evaluation result for thedata string of the predicted difference values.
 9. The coded datageneration method as claimed in claim 8 wherein the data string of thedeflection angles is classified into blocks corresponding to statetransition patterns of the deflection angles and a prediction expressionto convert the deflection angles into predicted difference values isselected for each of the blocks.
 10. The coded data generation method asclaimed in claim 8 wherein the data string of the deflection angles isclassified into blocks each containing a predetermined number of datapieces of the deflection angles and a prediction expression to convertthe deflection angles into predicted difference values is selected foreach of the blocks.
 11. The coded data generation method as claimed inclaim 8 wherein the data string of the deflection angles is classifiedinto blocks matching the change points of the resample length of theresampling in said step (1) and a prediction expression to convert thedeflection angles into predicted difference values is selected for eachof the blocks.
 12. The coded data generation method as claimed in claim8 wherein a prediction expression to convert the attention deflectionangle into a predicted difference value is selected in response to theevaluation result for the data string of the predicted difference valuesof a predetermined number of deflection angles preceding the attentiondeflection angle.
 13. The coded data generation method as claimed inclaim 12 wherein as many deflection angles as the predetermined numberare converted into a plurality of data strings of predicted differencevalues using the plurality of selection expressions and only if theevaluation result for the data string of the predicted difference valuesbased on a predetermined selection expression satisfies a predeterminedrequirement, the currently used prediction expression is changed to thepredetermined prediction expression and then the attention deflectionangle is converted into a predicted difference value.
 14. The coded datageneration method as claimed in claim 8 wherein in the attentiondeflection angle or block, a prediction expression is selected withreference to the prediction expression selection state in the deflectionangles or blocks preceding and following the attention deflection angleor block.
 15. The coded data generation method as claimed in claim 14wherein in the attention deflection angle or block, if a predictionexpression different from the prediction expressions adopted in thedeflection angles or blocks preceding and following the attentiondeflection angle or block is selected, a penalty value is added to theevaluation value of the evaluation criterion of the predicted differencevalue string in the attention deflection angle or block.
 16. The codeddata generation method as claimed in claim 15 wherein the penalty valueis set in response to the occurrence frequency of each predictionexpression.
 17. The coded data generation method as claimed in claim 2wherein the data string of the predicted difference values is evaluatedaccording to the number of 0s contained in the data string and theprediction expression with the largest number of 0s is selected.
 18. Thecoded data generation method as claimed in claim 2 wherein the datastring of the predicted difference values is evaluated according to thestatistical value of the predicted difference values contained in thedata string and a prediction expression is selected based on the resultof the statistical value.
 19. The coded data generation method asclaimed in claim 18 wherein the statistical value is at least either ofvariance and standard deviation of the predicted difference values. 20.The coded data generation method as claimed in claim 2 wherein theevaluation value for each predicted difference value is preset and thedata string of the predicted difference values is evaluated according tothe sum value of the evaluation values the predicted difference valuescontained in the data string.
 21. The coded data generation method asclaimed in claim 20 wherein the occurrence frequency of the predicteddifference value is the evaluation value and the prediction expressionwith the largest sum value is selected.
 22. The coded data generationmethod as claimed in claim 20 wherein the code length of the predicteddifference value is the evaluation value and the prediction expressionwith the smallest sum value is selected.
 23. An information transmissionapparatus comprising: a shape data resample processing section forresampling a linear object for setting a plurality of nodes andarranging position data of each node represented by a deflection anglefrom the immediately preceding node to generate a data string of thedeflection angles; a prediction expression determination section, whenthe data string of the deflection angles is converted into predicteddifference values indicating the difference from a predicted value topredict the position data of each of the nodes, for evaluating the datastring of the predicted difference values and selecting a predictionexpression to calculate the predicted value from among a plurality ofprediction expressions based on the evaluation result; and avariable-length coding processing section for converting each deflectionangle contained in the data string of the deflection angles generated bysaid shape data resample processing section into a predicted differencevalue from the predicted value calculated using the predictionexpression determined by said prediction expression determinationsection and variable-length coding a data string of the predicteddifference values.
 24. An information transmission apparatus comprising:a coded data decoding section for decoding variable-length coded datarepresenting position information of a linear object and reproducingshape data containing a data string of difference values each indicatingthe difference between a deflection angle and a predicted value; aprediction expression determination section for determining theprediction expression used to calculate the predicted value from theprovided shape data; and a shape data reconstruction section forcalculating a predicted value using the prediction expression determinedby said prediction expression determination section and reproducingposition information of nodes of the linear object from the data stringof the predicted difference values provided by said coded data decodingsection.
 25. A program recorded on a computer-readable medium thatexecutes generation of code data provided by coding a linear object,said program comprising: resampling a linear object for setting aplurality of nodes and arranging position data of each node representedby a deflection angle from the immediately preceding node to generate adata string of the deflection angles; when the data string of thedeflection angles is converted into predicted difference valuesindicating the difference from a predicted value to predict the positiondata of each of the nodes, evaluating the data string of the predicteddifference values; selecting a prediction expression to calculate thepredicted value from among a plurality of prediction expressions basedon the evaluation result; and converting each deflection angle containedin the data string of the deflection angles into a predicted differencevalue from the predicted value calculated using the determinedprediction expression and variable-length coding a data string of thepredicted difference values.
 26. A program recorded on acomputer-readable medium that decodes code data representing a linearobject, said program comprising: decoding variable-length coded datarepresenting position information of a linear object and reproducingshape data containing a data string of difference values each indicatingthe difference between a deflection angle and a predicted value;determining the prediction expression used to calculate the predictedvalue from the provided shape data; and calculating a predicted valueusing the determined prediction expression and reproducing positioninformation of nodes of the linear object from the provided data stringof the predicted difference values.